Research

Graduated from MIT with a focus on Applied Mathematics & Financial Engineering, completing doctoral research in high-frequency trading and market microstructure. Published multiple papers on dark pool liquidity capture and algorithmic matching mechanisms, widely cited by leading financial journals.

Career

At Sequoia Capital, Peters led the quantitative research team building cross-market arbitrage and high-frequency trading models. He designed the Dark Pool Execution Framework, achieving institutional-grade hidden liquidity capture while minimizing market impact on large orders. As the founding mind of the Dark Pool Strategy System, he pioneered the combination of AI risk control with multi-factor stock selection — a strategy that has consistently outperformed the market average over the past five years.

Leadership

A key figure in Sequoia’s quantitative trading division, Peters is frequently invited to speak at international financial summits and quant research forums. Under his leadership, Sequoia’s quant unit (Deep Quant) is regarded as one of the most innovative and influential trading teams in the industry.